Interest Rate Volatility And Nigeria’s Manufacturing Sector Odeleye & Taiwo 133 INTEREST RATE VOLATILITY AND NIGERIA’S MANUFACTURING SECTOR PERFORMANCE Anthonia T. Odeleye, Ph.D Department of Economics, University of Lagos, Akoka-Yaba, Lagos, Nigeria. antileye@yahoo.com, aodeleye@unilag.edu.ng & Taiwo H. Sangodele Alpha.beta consulting LLP, Water Corporation House, Ijora Causeway, Lagos. Abstract The manufacturing sector in Nigeria is faced with several economic challenges among which interest rate volatility takes a pole position. This study focuses on determining the effect of interest rate fluctuation on Nigeria’s manufacturing sector performance between 1980 and 2016. Its theoretical footing is the neo-classical or the loan-able fund theory, as it employs the Vector Error Correction framework to analyse the various data sourced from the World Development Indicators. The stationarity test confirmed that all the data were stationary at levels. Empirical results confirmed that interest rate has negative impact on manufacturing value added and manufacturing capacity utilisation in Nigeria. It was also observed that a long run relationship exists between interest rate fluctuation and manufacturing sector performance in Nigeria. It was therefore recommended that the regulatory authorities should strive to maximize this opportunity to design appropriate long term policies that can enhance industrial expansion through the appropriate interest rate regime rather than putting in place short term measures that are not sustainable to industrial development. Key words: Volatility, Interest rate, Manufacturing sector, Capacity utilisation, VECM, Nigeria. Jel Codes: D51, E52, E62, O4. Introduction Interest rate is an important economic price inspite of different viewpoints, since it determines the level of investment in any economy. It is seen as cost of capital or opportunity cost of funds. It also has fundamental implications for the economy, as it impacts on cost of capital or influencing availability of credit, or increasing savings, it is known (Tomola et al, 2012). Any economy comprises four interrelated sectors, operating to ensure that resources are best utilised in the production of goods and services to maximise the welfare of its citizenry. They are the financial, fiscal/government, external and real (i.e. the manufacturing sector). While the first four sectors have important roles in the welfare of the citizenry, the role of the manufacturing sector is particularly significant and strategic (Adebiyi, 2001; Tomola et al, 2012). It is the sector responsible for the production and distribution of goods and services (from a combination of factor resources), necessary to meet the consumption demand of an economy. It drives economic growth & development, and provides an indication on the living standard of the citizens of an economy and the effectiveness of government’s macroeconomic policies (Omotola, 2016). Further, it facilitates the creation of economic linkages with other sectors and helps in capacity building, employment and income generation. In view of this, a discussion of the real sector is mailto:antileye@yahoo.com mailto:aodeleye@unilag.edu.ng Interest Rate Volatility And Nigeria’s Manufacturing Sector Odeleye & Taiwo 134 topical as it is the pillar upon which the government’s objective of inclusive growth and poverty alleviation hinges, since it contributes mostly to employment generation and growth (CBN, 1998; Acha, 2011).The increasing importance of the real (manufacturing) sector to national development cannot be better captured than now when cataclysmic pricing upheavals in the global oil market are rapidly eroding the very foundation upon which Nigeria’s acclaimed growth was anchored in the past few years. Indeed, experts believe that one of the sure ways Nigeria could overcome the prevailing fiscal whirlwind is for government to focus more on the real sector, the strongest link in national economic diversification value chains with a view to tapping its immense benefits for broad-based development of the country. Specifically, far better than other climes’ real sector capacities, economic development experts see the Nigerian real sector as structurally strong in productivity mix such that exploring it better will give the country a stronger pedestal for capital accumulation, employment generation and economies of scale maximization (Ikhide and Alawode, 2001). According to a 2014 economic report by Renaissance Capital (Rencap), Nigeria’s manufacturing sector has displaced Telecommunications and Oil & Gas sectors to become the major economic growth driver. The report stated that with Nigeria’s re-based Gross Domestic Product, the manufacturing sector was growing faster than the telecommunications, oil and gas and agricultural sectors (Ikhide and Alawode, 2001; Chigbu, 2007). Their research is further strengthened by recent figures by the Manufacturing Association of Nigeria, MAN, which shows that there was an increase in capacity utilisation from 46.3 per cent recorded in the first half of 2013 to 52.7 per cent in the second half of last year, but is currently put at to 55 percent. Rencap states that the manufacturing sector recorded 22 per cent growth in 2013, as against the 14 per cent it recorded in 2012, with growth largely driven by the textile, cement and food sub-sectors, among others (Ikhide and Alawode, 2001; Chigbu, 2007; Omotola, 2016). The growth recorded by the manufacturing sector within the period under review, accounted for one third of the total growth in the economy. Interest rate as policy instrument can be used to foster meaningful macroeconomic stability. The rate of interest exacts its influence on the macro economy by transmitting through savings, investment, output, employment, money supply and balance of payment. As a return on investment in financial assets, interest rate serves as incentive to save and by extension influences the availability of savings. On the other hand, as cost of capital, interest rate affects the demand for and supply of credit (loanable funds). Changes in the rate of interest influences investment for expansion, machinery and equipment. Interest rate facilitates the mobilization of funds from surplus spending units to deficit spending units of the economy for efficient utilization of such funds for the enhancement and growth of industry. It is obvious thatinterest rate changes affect profitability and economic values of firms in various forms (Akinlo, 2005). In addition, the interest rate risk significantly relates to environmental risks such as changes in monetary, fiscal and economic policies of government with the aim of managing the national financial market.The manufacturing sector in Nigeria is often faced with several economic challenges among which interest rate takes a pole position because money is not readily available for manufacturers anywhere in the world and when it is available through bank loans, the interest rates can be so high to the extent that it becomes inimical to the success or otherwise of the business venture. It is evident that high lending rate discourages borrowing for investment and consequently has implications for the performance of the manufacturing sector. For instance, the food, beverage and tobacco sector account for half of the manufacturing sector in Nigeria (CBN, 1998). This subsector was able to accelerate its growth to 12 per cent in 2013, against 7 per cent in 2012 (Omotola, 2016).However, since 2013, the food and beverage sector has not been able to take its leap further owing to several factors among which is the high cost of capital in the country. It is in this light that this research work aims to determine the effect that interest rate has on Nigeria’s Interest Rate Volatility And Nigeria’s Manufacturing Sector Odeleye & Taiwo 135 manufacturing sector performance between 1980 and 2016 with the aim of recommending appropriate policies that could salvage the situation and boost manufacturing output in the country. The time frame chosen for the analysis is based on the availability of data. This study examines the interest rate volatility-economic performance nexus of manufacturing sector in Nigeria during a period between 1980 and 2016. It has five sections. Following the introduction is section two, which reviews relevant literature, while method of analysis employed is discussed in section three. Section four focuses on analysis and discussion of empirical results while conclusion and recommendation feature in section five. Literature Review Theoretical Review The Loan-able Funds Theory of Interest Rate The neo-classical or the loan-able fund theory owns its origin to the Swedish economist, Knut- Wicksell. It examines interest rate in terms of demand and supply of loan-able funds or credit. According to this theory, the rate of interest is the price of credit is determined by the demand and supply for loan-able funds. In other words, it is the price which equates the supply of credit, or saving plus the net increase in the amount of money in a period, to the demand for credit, or investment plus net hoarding in the period (Afolabi, 1999). The demand for loan-able fund has primarily three sources: government, businessmen and consumers who need them for purpose of investment, hoarding and consumption. The government borrows funds for provision of public goods or security. The businessmen borrow for capital goods and investment projects. Such borrowings are interest elastic and depend mostly on the expected rate of profit as compared with the interest rates. The demand of loan-able fund on the part of consumers is for the purchase of durable consumer goods, individuals’ borrowings and they are also interest elastic. The tendency to borrow is more at a lower rate of interest than at a higher rate (Afolabi, 1999; Chigbu, 2006). Although, this theory is superior to the classical interest rate theory yet, it has been criticised on the following grounds: it is indeterminate. Keynes (1936) faults its unrealistic assumption of full employment. It ignores the marginal efficiency of capital while its assumption of constant national income is unreliable. Empirical Review Akintelu et al (2016) investigate the determinants of capacity utilization in the Nigerian manufacturing sector between 1975 and 2008. The study uses capacity utilization as the dependent variable while its regressors are: real manufacturing output growth rate, real interest rate, consumer’s price index, fixed capital formation in manufacturing sector and electricity generation rate. Co- integration and Error Correction Model were employed as the estimation techniques so as to study the time series properties of the variables and to ascertain the existence of long-run relationship between capacity utilization and its determinants. Structured questionnaire was administered to assess the operational materials and the performance of the selected firms. The findings of the study reveal that there is positive relationship between consumer’s price index, fixed capital formation in manufacturing sector and capacity utilization. The study also shows that there is negative relationship between electricity generation, real manufacturing output growth rate and capacity utilization which resulted in low manufacturing productivity growth rate in Nigeria. Nwandu (2015) examined the impact of monetary policy variables on manufacturing in Nigeria from 1981 – 2012. The theoretical relationship between monetary policy variables and manufacturing sector (that is, the real sector) was critically examined and established in this study using the Johansen co-integration test in order to establish long run equilibrium relationship between the Interest Rate Volatility And Nigeria’s Manufacturing Sector Odeleye & Taiwo 136 explained and the explanatory variables. The error correction model was employed to estimate the model and the study reveals that money supply and credit to private sector exert tremendous influences on manufacturing in Nigeria. Imoughele and Ismaila (2014) investigated the impact of monetary policy on Nigeria’s manufacturing sector performance for the period 1986-2012. Results of the study after ensuring data stationarity and co-integration show that the individual variables: external reserve, exchange rate and inflation rate were statistically significant to manufacturing sector output while broad money supply and interest rate were not statistically significant to manufacturing sector output in the previous and current year. However, interest rate, exchange rate and external reserve impacted negatively on the sector output but broad money supply and inflation rate affect the sector positively. The pair-wise Granger Causality results suggest that real exchange rate and external reserves granger cause Nigeria’s manufacturing output. Ayanwale et al (2013) notes that the Central Bank of Nigeria (CBN) has not formulated a model that will reduce interest rate, inflation and stabilize the exchange rate and as such set out to examine the impact of interest rates on the development of an emerging market using a time series analysis of 40 years (1970- 2010).The Error Correction Modelling (ECM) was adopted to reconcile fluctuations or changes both in the short and long runs between the variables. The non-zero coefficient of changes in interest rate and exchange rate in both ways been statistically significant indicates a short-run causality from interest rate to gross fixed capital formation as well as from changes in inflation to gross domestic product. Thus, the paper recommends that pragmatic approach needs to be adopted to ensure that the lending rates are reduced to single digit in order to reduce production cost, high unemployment rate and encourage foreign direct investment (FDI). In addition, Odior (2013) investigated the impact of macroeconomic factors on manufacturing productivity in Nigeria over the period 1975-2011. The study finds that credit to the manufacturing sector in the form of loans and advances and foreign direct investment have the capacity to sharply increase the level of manufacturing productivity in Nigeria while broad money supply has less impact. The study, therefore, recommends that government must create ‘’enabling environment’’ for manufacturers in the area of infrastructure, financial, legal and property rights. Idoko et al (2012) assessed the impact of interest rate deregulation on economic growth in Nigeria. Using an autoregressive model, GDP growth rate was regressed against lending rate, savings rate, inflation rate, exchange rate, financial deepening and lag for two separate periods; the regulation era (1970- 1986) and deregulation era (1987–2009). The results show that deregulated interest rate has an insignificant impact on economic growth. However, inflation rate and exchange rates were found to have positive and significant impact on economic growth Obamuyi et al (2010) assess the effect of bank lending and economic growth on the manufacturing output in Nigeria. Times series data covering a period of 36 years (1973-2009) were employed and tested with the co-integration and vector error correction model (VECM) techniques. The findings of the study show that manufacturing capacity utilization and bank lending rates significantly affect manufacturing output in Nigeria. Eregha (2010) examined variations in interest rate and investment determination in Nigeria and deduced that investment has an indirect relationship with interest rate variation. Olubanjo, Atobatele and Akinwumi (2010) simulated the inter-relationships among interest rates, savings and investment in Nigeria between 1993 and 2010 using two stages least square method. Their result suggests that a marked decrease in the real lending rate would not result automatically into increased domestic investment. In the same vein, Khrawish et al (2010) in their study in Jordan Interest Rate Volatility And Nigeria’s Manufacturing Sector Odeleye & Taiwo 137 on stock investment (based on the monthly datafrom January 1988 to March 2003) found that interest rate exerts significant negative relationship with share price for markets of Australia, Bangladesh, Canada, Chile, Colombia, Germany, Italy, Jamaica, Japan, Malaysia, Mexico, Philippine, South Africa, Spain, and Venezuela. They argued on the availability of significant negative relationship between changes of interest rate and changes of share price in only six countries from the sample. Omole and Falokun (1999) examined the problem and identified domesticfactors such as inflation, growth in equity and exchange rates as key factors explaining interest rate variability in Nigeria. Horgan (2014) investigated the impact of interest rate and foreign exchange rates on manufacturing sub sector in Nigeria during the period 1980 -2012. The study adopted the OLS and the Co integration techniques on data on index of manufacturing sector output, rate, and FDI and government capital expenditure. The study reveals that interest rate in the long run does not impact on manufacturing output while government capital expenditure does. Udoka and Anyingang (2012) investigated the effect of interest rate fluctuation on the economic growth of Nigeria before and after interest rate deregulation regime in Nigeria. Data collected were analyzed using the ordinary least square (OLS) analytical technique. The result of the findings reveal that, there existed an inverse relationship between interest rate and economic growth in Nigeria. Hence, they concluded that increase in interest rate decreases GDP growth in Nigeria, thus retarding growth of the real sector. In addition, Udoka and Roland (2012) carried out a study on the determinant of interest rate using the OLS technique and the long linear relationship between real balance and its determinants. The study specified and estimated a short term demand for money function that related real balance to aggregate real national income, lagged real balances and a variety of interest rates, Central Bank short-term interest rate, time deposit rate, and savings deposit interest rate. A war dummy was included to account for the civil war year 1967-1969. The study concludes that short-run and long- run interest elasticity of demand of currency is not significantly different from zero while the short- run income elasticity is in all cases much greater than unity; and that for demand deposit, the interest elasticity are very low and insignificant, while the short-run income elasticity was never below 0.8 and the long-run elasticity was generally about 1.4. Loto (2012) examined the implications of interest rate for savings and investment in Nigeria. Data were analyzed using Pearson’s Correlation Coefficient and the ordinary least square technique. Evidence from the study showed interest rate as a poor determinant of savings and investment indicating that bank loans are mostly not used for productive purposes. The study recommends that bank loans should be channelled to productive investments if interest is to play its catalytic role in the Nigerian economy. Obviously, the results of the above reviewed studies do not indicate any consensus on the impact of interest rate volatility on performance of manufacturing sector in Nigeria. However, most of these studies are largely descriptive, while those that employed econometric techniques did not take into consideration statistical properties of the series, as they relate to stationarity of the individual series and co-integration among linear combinations of the series, before applying the least square techniques in estimating their models. In addition, they restricted their scope to 2012, we extend ours to 2016 to further examine whether the reverse is now the case. Moreover, most of the previous studies have employed the ordinary least squares technique and error correction framework, this study employs the Vector Autoregression framework to determine the effect of interest rate on the various indicators of performance in the manufacturing sector. Interest Rate Volatility And Nigeria’s Manufacturing Sector Odeleye & Taiwo 138 Methods of Analysis The vector auto-regression/vector error correction (VAR)/VECM model is the most suitable for this work. This is because the study focuses on how the lags of interest rate affect the three indicators of manufacturing sector in Nigeria. Also, the VAR/VECM model makes it possible for each equation to be estimated with the usual OLS method separately and forecasts obtained. VAR/VECM models are in most cases better than those obtained from complex simultaneous equation models (Mahmoud, 1984; McNees, 1986). Moreover, the VAR/VECM model is employed in this study to be able to test for the direction of causality among the variables,overcome the problem of endogeneity that may arise and to enable us determine the effect of interest rate fluctuation on manufacturing sector performance in Nigeria. Model Specification In order to carry out this research with a robust analysis on the determination of the effect of interest rate fluctuation on Nigeria’s manufacturing sector, we modify the Solow growth model by replacing the output per effective labour with manufacturing output, manufacturing value added and manufacturing capacity utilisation, while capital per effective labour is replaced with interest rate in order to align with the objectives of the study. It relies Solow (1956) version of the Neo-classical model more suitable in this study owing to its dynamism.The Solow model focuses on four variables: Output (Y), Capital (K), labour (L), and “knowledge” or the effectiveness of labour (A). At any point, the economy has some of amount of capital, labour and knowledge (Solow, 1956). These are combined to produce output. The production function takes the form of: (1) Where Yt = output at time t, Kt = capital at time t, Lt = labour at time t, At = knowledge at time t. At and Lt enter the model multiplicatively, hence At*Lt is effective labour. The model suggested that if the amount of knowledge (A) increases, there is technology progress. Hence, in an explicit form, equation (1) is written as: (2) 0< α< 1 (3) (4) Therefore, (5) From above, y is output per effective labour and k is capital per effective labour. In the specified model in equations 6 to 9, the semi-log is employed. The reason is because it enables us to reduce the large values associated with each of the variables and also makes the coefficients of the regressed parameters to be smaller and reflect real life situations. However, interest rate is not logged since it is already in rate form and its value is already small. The VAR/VECM model to be estimated is stated as follows: LMVD= α + β1 (INT)t-1 + β2 (LMQ)t-1 +β3 (LMCU)t-1 + β4 (LMVD)t-1 +ε1 (6) LMQ= α + β1 (INT)t-1 + β2 (LMQ)t-1 +β3 (LMCU)t-1 + β4 (LMVD)t-1 +ε2 (7) LMCU= α + β1 (INT)t-1 + β2 (LMQ)t-1 +β3 (LMCU)t-1 + β4 (LMVD)t-1 +ε2 (8) INT= α + β1 (INT)t-1 + β2 (LMQ)t-1 +β3 (LMCU)t-1 + β4 (LMVD)t-1 +ε2 (9) Where: LMVD = Log of manufacturing value added; LMCU is log of manufacturing capacity utilisation; LMQ represents log of manufacturing output; while INT isInterest rate. α represents the intercept term; β1,β2, β3 and β2 are the slope parameters; while εt is the error term. Our research design for the study is based on a multiple regression, which is estimated by employing the VAR/VECM model. Secondary data used cover the period between 1980 and 2016; are sourced from World Development Indicators (WDI, 2016) and Central Bank of Nigeria Statistical Bulletins (CBN, 2016). The data include manufacturing value added, manufacturing output, manufacturing capacity utilisation and interest rate. The study is tested for co-integration using the Johansen Interest Rate Volatility And Nigeria’s Manufacturing Sector Odeleye & Taiwo 139 approach which is suitable for VAR/VECM model. The appropriate lag length chosen is based on the Akaike Information. After estimating the VAR/VECM result, the Variance Decomposition analysis was conducted to examine the response of the dependent variables in the VAR/VECM to fluctuations in interest rate. Results and Discussion Empirical results are presented with the unit root test of stationarity and Johansen co-integration test of long-run relationship among the variables. Augmented Dickey-Fuller (ADF) Test for Stationarity Table 1: ADF Unit Root Test Variable ADF Tau Statistic (Linear Trend) Order of Integration MVD -5.746957**(0) [-3.557759] I(1) INT -5.327976**(0) [-3.557759] I(1) MQ -6.433455**(0) [-3.557759] I(1) MCU -4.861650**(0) [-3.557759] I(1) Note: ** significant at 5%; Mackinnon critical values and are shown in parenthesis. The lagged numbers shown in brackets are selected using the minimum Schwarz and Akaike Information criteria. Source: Authors’ Computation from Eviews 9. The unit root test result presented in table 1 shows that manufacturing value added; interest rate, manufacturing output, and manufacturing capacity utilisation are all stationary at first difference for linear trend test model. This indicates that those incorporated series in the regression model have no unit root, implying that the series are mean reverting and convergence towards their long-run equilibrium. Johansen Co-integration Test for Long Run Relationship Table 2: Unrestricted Co-integration Rank Test (Trace) Hypothesized No. of CE(s) Eigenvalue Trace Statistic Critical Value (0.05) Prob.** None * 0.848576 88.84884 47.85613 0.0000 At most 1 0.377393 24.66807 29.79707 0.1737 At most 2 0.134659 8.557493 15.49471 0.4079 At most 3 0.101528 3.640023 3.841466 0.0564 Trace test indicates 1 co-integrating eqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Source: Authors’ Computation from Eviews 9. Table 3: Unrestricted Co-integration Rank Test (Maximum Eigenvalue) Hypothesis No. of CE(s) Eigen value Max-Eigen Statistic Critical Value (0.05) Prob.** None * 0.848576 64.18077 27.58434 0.0000 At most 1 0.377393 16.11058 21.13162 0.2184 At most 2 0.134659 4.917471 14.26460 0.7522 At most 3 0.101528 3.640023 3.841466 0.0564 Max-eigenvalue test indicates 1 cointegratingeqn(s) at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values Source: Authors’ Computation from Eviews 9. Interest Rate Volatility And Nigeria’s Manufacturing Sector Odeleye & Taiwo 140 In table 2, the trace test result presented shows that one cointegrating equation exists among the variables. Also, the maximum-eigen test presented in table 3 shows that there is one co-integrating equation among the variables. We therefore reject the null hypothesis of no co-integration at 5% level of significance. The results therefore indicate that there is long-run relationship between manufacturing capacity utilisation, manufacturing output, manufacturing value added and interest rate between 1980 and 2016 in Nigeria. VECM Results Table 4: VECM with MCU as Dependent Variable Source: Authors’ Computation from Eviews 9. In the result presented in table 4, where manufacturing capacity utilisation is presented as the dependent variable. Results show that interest rate fluctuation has negative impact on manufacturing capacity utilisation in Nigeria. In the first period, a one percent increases in interest rate causes the capacity utilisation in the manufacturing sector to fall by about 11.8% while holding constant other explanatory variables. Also, the second lag of interest rate shows that if interest rate increases by 1%, manufacturing capacity utilisation will fall by about 33.9% while keeping constant other regressors. This means that a higher interest rate is inimical for manufacturing capacity utilisation in Nigeria. The R-squared suggests that it is a very good model as about 80% variation in the dependent variable is explained by the regressors in the model while after removing the effect of insignificant estimators, adjusted R-squared value of 0.728 shows that about 72.8% of variation is still explained in the model. The error correction term is also negative and significant, which shows that the VECM model estimated conforms to apriori expectation. That is, the short run error in the variables after first differencing the data is expected to be temporal as the variables are expected to return to their long run path while the speed of adjustment is about 1.4% as suggested by the coefficient of the ECM(-1) term. Variable Coefficient Std. Error t-Statistic Prob. ECM(-1) -0.014901 0.002688 -5.542688 0.0000 INT(-1) -0.118695 0.046932 -2.529071 0.0187 INT(-2) -0.339995 0.048170 -7.058198 0.0000 MCU(-1) -0.197294 0.099166 -1.989532 0.0587 MCU(-2) -0.453803 0.097766 -4.641743 0.0001 MQ(-1) 0.012916 0.277502 0.046545 0.9633 MQ(-2) 0.142797 0.269663 0.529539 0.6015 MVD(-1) 0.466588 0.389199 1.198840 0.2428 MVD(-2) 0.293476 0.401840 0.730331 0.4726 C -0.085046 0.028757 -2.957379 0.0071 R-Squared 0.804506 F-statistic 10.51675 Adj. R-squared 0.728009 Prob(F-statistic) 0.000003 Interest Rate Volatility And Nigeria’s Manufacturing Sector Odeleye & Taiwo 141 Table 5: VECM with MQ as Dependent Variable Source: Authors’ Computation from Eviews 9. In the result presented in table 5, where manufacturing output is presented as the dependent variable. Results show that interest rate fluctuation has negative impact on manufacturing output in Nigeria. In the first period, a one percent increases in interest rate causes the output in the manufacturing sector to fall by about 5.6% while holding constant other explanatory variables. Also, the second lag of interest rate shows that if interest rate increases by 1%, manufacturing output will fall by about 8.5% while keeping constant other regressors. This means that a higher rate of interest rate hinders manufacturing output in Nigeria. The R-squared suggests that it is a very good model as about 60% variation in the dependent variable is explained by the regressors in the model while after removing the effect of insignificant estimators, adjusted R-squared value of 0.557 shows that about 55.7% of variation is still explained in the model. The error correction term is also negative and significant, which shows that the VECM model estimated conforms to apriori expectation. That is, the short run error in the variables after first differencing the data is expected to be temporal as the variables are expected to return to their long run path while the speed of adjustment is about 0.7% as suggested by the coefficient of the ECM(-1) term. Variable Coefficient Std. Error t-Statistic Prob. ECM(-1) -0.007171 0.003092 -2.319549 0.0296 INT(-1) -0.056934 0.053972 -1.054866 0.3024 INT(-2) -0.085508 0.055396 -1.543581 0.1363 MCU(-1) 0.080163 0.114042 0.702924 0.4892 MCU(-2) -0.102385 0.112431 -0.910645 0.3719 MQ(-1) 0.503713 0.319129 1.578401 0.1281 MQ(-2) 0.225191 0.310114 0.726155 0.4751 MVD(-1) -0.913058 0.447582 -2.039979 0.0530 MVD(-2) -0.843237 0.462119 -1.824717 0.0811 C -0.036186 0.033071 -1.094175 0.2852 R-Squared 0.609812 F-statistic 13.93989 Adj. R-squared 0.557129 Prob(F-statistic) 0.000006 Durbin-Watson 1.957387 Interest Rate Volatility And Nigeria’s Manufacturing Sector Odeleye & Taiwo 142 Table 6: VECM with MVD as Dependent Variable Source: Authors’ Computation from Eviews 9. In the result presented in table 6, where manufacturing value added is presented as the dependent variable. Results show that interest rate fluctuation has negative impact on manufacturing value added in Nigeria. In the first period, a one percent increase in interest rate causes the value added in the manufacturing sector to fall by about 4.4% while holding constant other explanatory variables. Also, the second lag of interest rate shows that if interest rate increases by 1%, manufacturing value added will fall by about 5.0% while keeping constant other regressors. This confirms earlier results by suggesting that a higher rate of interest adversely affect manufacturing value added in Nigeria. The R-squared suggests that it is a very good model as about 63% variation in the dependent variable is explained by the regressors in the model while after removing the effect of insignificant estimators, adjusted R-squared value of 0.619 shows that about 61.9% of variation is still explained in the model. The error correction term is also negative and significant, which shows that the VECM model estimated conforms to apriori expectation. That is, the short run error in the variables after first differencing the data is expected to be temporal as the variables are expected to return to their long run path while the speed of adjustment is about 0.4% as suggested by the coefficient of the ECM(-1) term. Table 7: Variance Decomposition of Interest Rate in Nigeria (1980-2016) Period S.E. INT MCU MQ MVD 1 0.351887 100.0000 0.000000 0.000000 0.000000 2 0.523343 99.50671 0.000456 0.014546 0.478284 3 0.652042 98.89716 0.288771 0.187782 0.626291 4 0.769327 98.55088 0.391232 0.529948 0.527945 5 0.857572 98.53545 0.322773 0.706000 0.435774 6 0.940861 98.44910 0.284424 0.890532 0.375943 7 1.029624 98.33253 0.276878 1.037921 0.352676 8 1.112860 98.25194 0.267266 1.140620 0.340177 9 1.190417 98.13293 0.265290 1.274617 0.327166 10 1.264935 98.00524 0.263439 1.414955 0.316366 Source: Authors’ Computation from Eviews 9. Variable Coefficient Std. Error t-Statistic Prob. ECM(-1) -0.004208 0.002118 -1.986799 0.0590 INT(-1) -0.044595 0.036973 -1.206158 0.2400 INT(-2) -0.050388 0.037948 -1.327817 0.1973 MCU(-1) -0.013291 0.078122 -0.170130 0.8664 MCU(-2) -0.105043 0.077019 -1.363858 0.1858 MQ(-1) 0.140445 0.218613 0.642436 0.5269 MQ(-2) 0.335735 0.212438 1.580387 0.1277 MVD(-1) -0.390175 0.306608 -1.272554 0.2159 MVD(-2) -0.711558 0.316566 -2.247739 0.0345 C -0.066039 0.022655 -2.914998 0.0078 R-Squared 0.632139 F-statistic 12.96660 Adj. R-squared 0.619063 Prob. (F-statistic) 0.000035 Durbin-Watson 1.957387 Interest Rate Volatility And Nigeria’s Manufacturing Sector Odeleye & Taiwo 143 As presented in Table 7, the variance decomposition of interest rate shows that interest rate itself accounted for all its shocks in the first period. However, in period 2, manufacturing value added accounted for more of the shocks to interest rate after interest rate itself. In period 3, manufacturing capacity utilisation was more affected by interest rate than the two other indicators of manufacturing sector performance. However, from the 4th to the 10th period, manufacturing output is the worst hit by interest rate fluctuation. This means that interest rate fluctuation in Nigeria has a significant impact on manufacturing output and consequently manufacturing sector performance. This is currently the situation in Nigeria as higher rate of interest has severely affected the productivity of the manufacturing sector by restricting access to funds for the private sector and forcing several of the manufacturing firms to lay off workers and even shut down operations owing to inadequate financial resources. It is through a low manufacturing output that manufacturing value added declines and capacity utilisation in the sector also falls. The result of this study is well situated in literature as it has confirmed some of the results of previous studies such as Adebiyi (2001),Ahmed (2003), and Akintelu et al (2016)that there exists negative relationship between interest rate and manufactured output. The result of this empirical enquiry also confirmed the results of Ayanwale et al (2013), Imoughele and Ismaila (2014), and Nwandu (2015)that interest rate significantly impact manufacturing sector in Nigeria. Furthermore, this present study is in support of Akinlo (2005), Patterson and Okafor (2006), Idoko et al (2012), and Odior (2013), that there is long run relationship between interest rate and manufacturing sector performance. However, the result of this study is at variance with Obamuyi et al (2010), Eregha (2010), Khrawish et al (2010), Udo and Udeaja (2011), and Horgan (2014), which found positive relationship between interest rate fluctuations and manufacturing sector performance. This is because it is observed in this study that interest rate has negative impact on manufactured output, manufacturing capacity utilisation and manufacturing value added in Nigeria. Conclusion and Recommendations This study focuses on the effect of interest rate fluctuation on Nigeria’s manufacturing sector performance between 1980 and 2016. Over the period of the study, the manufacturing sector has witnessed some ups and downs vis-à-vis interest rate movement, hence, the necessity for this study. The model estimated in the study employed themanufacturing capacity utilisation (MCU), manufacturing value added (MVD), and manufacturing output (MQ) as the dependent variables, while interest rate (INT) is employed in the study as explanatory variable.The vector error correction model and variance decomposition analysis were employed as the estimation techniques in the study and findings include: (i) interest rate increases has a negative but significant impact on manufacturing value added in Nigeria; (ii) higher interest rate has a negative impact on manufacturing output in Nigeria; (iii) increases in interest rate cause the manufacturing capacity utilisation in Nigeria to fall; (iv) the unit root test results indicate that manufacturing capacity utilisation (MCU), manufacturing value added (MVD), manufacturing output (MQ), and interest rate (INT) are not stationary at levels, but when first differenced, they were all stationary; and (v) the Johansen co-integration test conducted confirmed that there is long run relationship between interest rate and manufacturing sector performance in Nigeria. The effect of interest rate fluctuation on manufacturing sector performance in Nigeria between 1980 and 2016 covered a period of thirty-seven years. The estimated regression results revealed that interest rate has negative effect on all the three indicators of the manufacturing sector in the country. The variance decomposition analysis suggests that interest rate volatility in Nigeria has a significant impact on manufacturing output and consequently manufacturing sector performance. This is currently the situation in Nigeria as higher interest rates being charged by the commercial banks have hindered the growth of the manufacturing sector by severely reducing their productivity and forcing Interest Rate Volatility And Nigeria’s Manufacturing Sector Odeleye & Taiwo 144 several of the manufacturing firms to lay off workers and even shut down operations. It is through a low manufacturing output that manufacturing value added declines and capacity utilisation in the sector also falls. Therefore, this study rejects the null hypotheses and concludes that interest rate fluctuation has a negative impact on the manufacturing sector in Nigeria over the study period. Considering the results of the effect of interest rate fluctuation on manufacturing sector performance in Nigeria between 1980 and 2016, the following policies are recommended: i. As it has been established that long run relationship exists between interest rate movement and the Nigerian manufacturing sector performance, it becomes expedient for regulatory authorities to maximize this opportunity to design appropriate long term policies that can enhance industrial expansion through the appropriate interest rate regime rather than putting in place short term measures that are not sustainable to industrial development. ii. 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